Marketplace status: live

AI Data Tasks, Operated by Verified Humans.

HiveLabel delivers scalable human-powered data collection, labeling, tagging, enrichment, validation and human-review workflows. Our professional marketplace connects AI enterprises, robotics companies and internet teams with global verified skilled contributors to generate high-quality, trustworthy training, evaluation and production datasets.

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Network status

Live

Contributor and AI team access is open.

Workflow types06

Quality modeHITL

Service statusLive

Task boardOpen

Platform workflow

From task definition to trusted dataset output.

HiveLabel is built for the actual operational needs of AI engineering teams. We standardize every data production link through clear task specifications, qualified human contributors, multi-layer quality validation and review-ready standardized export solutions, realizing full-cycle controllable AI data operation.

Define data task

Create customized data collection, labeling, tagging, enrichment, validation and human review tasks. Support independent configuration of label schemas, detailed task guidelines, quality acceptance standards, output formats and delivery cycles to match personalized project requirements of different AI business scenarios.

Route qualified contributors

Intelligently match tasks with pre-verified professional contributors based on task type, technical difficulty, domain expertise, language ability and qualification assessment results. Implement strict permission isolation for specialized and high-sensitive tasks, only open to contributors who pass professional skill certification.

Validate Output via HITL Quality Mechanism

Adopt mature Human-in-the-Loop (HITL) quality control system, including multi-person consensus verification, random spot audits, professional reviewer secondary inspection and threshold-based quality screening. Effectively filter low-confidence and non-compliant data to ensure overall dataset accuracy and consistency.

Export Reviewed Datasets

Export fully reviewed and accepted standardized dataset packages, which are directly compatible with downstream model training, algorithm evaluation, data fine-tuning and secondary enrichment workflows. Complete task-level traceability metadata is attached to all exported data to support enterprise compliance audit and data iteration analysis.

Products and services

Professional AI data service products & solutions.

We provide one-stop full-cycle AI data solutions covering self-service platform tools and fully managed customized services, helping enterprises solve all data pain points in AI model development, iteration and production.

Managed full-service data annotation

End-to-end outsourced annotation service for enterprise large-scale projects. Our professional team is responsible for contributor scheduling, guideline optimization, batch task execution, multi-round QA verification and final standardized dataset delivery. It is suitable for long-term and large-volume data production demands in computer vision, natural language processing, audio recognition and multimodal AI fields.

Self-service task marketplace

Independent operation platform for enterprise teams. Users can independently upload raw data, configure task templates, set quality review rules, monitor real-time task progress and complete one-click data export. Flexible capacity scaling supports small-batch trial production and medium-scale daily data iteration.

Custom real-world data collection

Provide targeted real-scene data collection services according to enterprise business demands, including image, video, speech, text and multimodal data gathering. Strictly comply with regional privacy compliance standards and complete legal subject authorization to ensure all collected data is commercially available.

Dataset validation & human review

Professional manual verification and optimization services for existing enterprise datasets and model output contents, including LLM response ranking, factual correctness inspection, content safety audit, annotation error correction and benchmark dataset screening, to improve model generalization and accuracy.

Enterprise API & system integration

Open standard REST API interface to realize seamless docking with enterprise ML workflow, cloud storage systems and internal management platforms. Support automatic task creation, real-time progress callback, reviewed data batch pull and audit log export, realizing intelligent and automated data operation.

Standard task category matrix

Task ID	Task type	Business category	Standard review mechanism
HV-IMG-001	Image bounding box annotation	Computer vision	Multi-pass QA verification
HV-CAT-014	Product attribute tagging	Commerce AI	Professional validator review
HV-LLM-022	LLM answer ranking & evaluation	Language AI	Multi-party consensus scoring
HV-AUD-006	Speech segment validation & tagging	Audio AI	Random spot audit

Quality and trust architecture

HITL quality control system, built for trustworthy AI data.

HiveLabel builds a full-link data quality guarantee system based on human-in-the-loop mechanism. Quality control runs through task assignment, execution, review and export, fundamentally ensuring dataset accuracy, consistency and availability.

Verified

Verified contributor qualification mechanism

Implement hierarchical skill assessment and real-name qualification certification for all platform contributors. Different levels of task access permissions are granted according to professional capabilities. Specialized and high-precision data tasks are only open to senior certified contributors to eliminate unprofessional operation risks.

Review

Multi-pass layered validation

Configure flexible multi-dimensional review rules including independent repeated annotation, peer consensus comparison and professional reviewer final inspection. Statistical spot audit is adopted for mass data to balance data quality and delivery efficiency.

Audit

Full task-level traceability

Establish complete data operation logs, including task instructions, contributor information, submission time, review records and final acceptance results. Full-link traceability supports enterprise compliance audit, data error analysis and subsequent model iteration optimization.

Boundary

Fine-grained access control

Adopt role-based permission isolation mechanism, strictly distinguish operation permissions of contributors, reviewers, project managers and enterprise clients. Realize task-scoped data access protection and effectively avoid sensitive data leakage risks.

Industry application use cases

Professional data solutions for multiple AI scenarios.

Computer vision labelingProvide full-type visual data annotation services such as bounding box, polygon segmentation, key point marking and video frame annotation, serving autonomous driving, robot vision, industrial inspection and visual classification model training.

LLM & generative AI reviewSupport LLM response ranking, pairwise comparison, factual verification, prompt optimization and RLHF human feedback data production, helping generative AI models improve accuracy, safety and user experience.

Structured data enrichmentAdd standardized metadata, attribute labels and classification tags for unstructured raw data to realize data structuring and lay the foundation for efficient model training and data mining.

E-commerce catalog taxonomy taggingStandardize product attributes, unify industry taxonomy standards, and build standardized e-commerce product datasets to support recommendation algorithms, commodity classification and intelligent search systems.

Audio & speech data validationComplete speech transcription verification, speaker distinction, audio event tagging and voice quality evaluation, providing high-quality data support for ASR speech recognition and TTS speech synthesis models.

Dataset verification & benchmark curationClean noisy data, correct annotation errors, screen high-quality samples, and build professional benchmark evaluation datasets for model performance testing and version iteration comparison.

Official pricing plans

Transparent & scalable pricing for all AI teams.

We provide tiered pricing solutions suitable for startup teams, growing enterprises and large-scale institutional clients. All plans include official platform basic functions and standard quality assurance services.

Plan

Starter project plan

Applicable scenarios: Small-scale AI pilot projects, prototype model training, small-batch dataset production.
Core privileges: Self-service marketplace access, standard HITL review pipeline, basic JSON/CSV data export, email technical support, pay-per-task flexible billing mode, no minimum order limit.

Most adopted

Business growth plan

Applicable scenarios: Medium-sized AI teams, long-term stable data iteration demands, multi-project parallel development.
Core privileges: All Starter plan functions plus official API interface access, priority technical support, customizable review rules, SLA delivery guarantee, real-time project data dashboard, batch data export.

Plan

Enterprise custom managed plan

Applicable scenarios: Large-scale commercial projects, compliance-sensitive data scenarios, customized data collection demands, long-term strategic cooperation.
Core privileges: Full platform function authorization, dedicated project manager one-to-one service, full-process customized solution, independent quality review team, complete compliance documents (DPA/audit logs), private deployment and system docking support, negotiable exclusive SLA.

Pricing notes

01 Conventional annotation tasks adopt pay-per-task consumption pricing, and the unit price is adjusted according to task complexity, review standard and language type.
02 Custom real-scene data collection projects are priced independently according to project scope, difficulty and compliance requirements.
03 Enterprise-level exclusive services and private deployment solutions support personalized quotation.

Request official quotation

For AI enterprise teams

Operational data workflows built for AI.

Core advantages

Different from single automated tools, HiveLabel focuses on AI industrial data operation, solving the pain points of unstable data quality and insufficient manual review capabilities in enterprise model iteration:

01 One-click launch of data collection, annotation, enrichment and human review tasks to meet full-cycle data demands.
02 Convert enterprise personalized task specifications into standardized executable work packets to reduce communication costs.
03 Embed professional human judgment links into model training, fine-tuning and quality inspection workflows to improve model robustness.
04 Export compliant and traceable high-quality datasets to seamlessly connect downstream AI production systems.

For global verified contributors

Standardized & high-quality AI data task ecosystem.

Core rights & rules

HiveLabel builds a standardized global contributor cooperation system, providing stable, transparent and high-quality AI data task resources for professional human workers:

01 Complete professional qualification assessment to unlock matched high-matching data tasks.
02 Complete labeling, tagging, enrichment and verification work in accordance with official standardized guidelines.
03 Accumulate personal reputation scores through high-quality work output and pass platform quality audits.
04 Obtain stable remuneration after work passes multi-layer official review and acceptance.

Secure line

Contact

Talk to the HiveLabel team.

Tell us whether you are building AI systems, contributing data work, or exploring a partnership.

News / product updates

Build notes from the HiveLabel team.

2026-06-15Contributor update